Neural network based vehicle dynamics model

    公开(公告)号:US11029693B2

    公开(公告)日:2021-06-08

    申请号:US15672207

    申请日:2017-08-08

    Applicant: TuSimple, Inc.

    Abstract: A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

    System and method for real world autonomous vehicle trajectory simulation

    公开(公告)号:US10739775B2

    公开(公告)日:2020-08-11

    申请号:US15796765

    申请日:2017-10-28

    Applicant: TuSimple, Inc.

    Abstract: A system and method for real world autonomous vehicle trajectory simulation are disclosed. A particular embodiment includes: receiving training data from a real world data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation, the vicinal scenarios corresponding to different locations, traffic patterns, or environmental conditions being simulated, provide vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions, generate a trajectory corresponding to perception data and the vehicle intention data, execute at least one of the plurality of trained trajectory prediction models to generate a distribution of predicted vehicle trajectories for each of a plurality of simulated vehicles of the simulation based on the vicinal scenario and the vehicle intention data, select at least one vehicle trajectory from the distribution based on pre-defined criteria, and update a state and trajectory of each of the plurality of simulated vehicles based on the selected vehicle trajectory from the distribution.

    System and method for autonomous vehicle control to minimize energy cost

    公开(公告)号:US10678234B2

    公开(公告)日:2020-06-09

    申请号:US15685715

    申请日:2017-08-24

    Applicant: TuSimple, Inc.

    Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

    System and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles

    公开(公告)号:US10308242B2

    公开(公告)日:2019-06-04

    申请号:US15640521

    申请日:2017-07-01

    Applicant: TuSimple

    Abstract: A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.

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